Advances in Explainable Artificial Intelligence (XAI): 3rd Edition

Dear Colleagues,

Recently, artificial intelligence has seen a shift in focus towards the design and deployment of intelligent systems that are interpretable and explainable, with the rise of a new field: explainable artificial intelligence (XAI). This has been echoed both in the research literature and in the press, attracting scholars from all around the world as well as a lay audience. Initially devoted to the design of post hoc methods for explainability, essentially wrapping machine- and deep-learning models with explanations, it is now expanding its boundaries to ante hoc methods for the production of self-interpretable models. Along with this, neuro-symbolic approaches for reasoning have been employed in conjunction with machine learning in order to extend modeling accuracy and precision with self-explainability and justifiability. Scholars have also started shifting the focus towards the structure of explanations since the ultimate users of interactive technologies are humans, linking artificial intelligence and computer sciences to psychology, human–computer interaction, philosophy, and sociology.

It is certain that explainable artificial intelligence is gaining momentum, and this Special Issue calls for contributions exploring this new fascinating area of research, seeking articles that are devoted to the theoretical foundation of XAI, its historical perspectives, and the design of explanations and interactive human-centered intelligent systems with knowledge–representation principles and automated learning capabilities, not only for experts but for the lay audience as well.

Dr. Luca Longo
Guest Editor

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